Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	33.0	1.873187
daal4py_KNeighborsClassifier	0.0	6.0	26.959547
KNeighborsClassifier_kd_tree	0.0	2.0	38.264852
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	31.523763
KMeans_tall	0.0	0.0	26.326621
daal4py_KMeans_tall	0.0	0.0	10.569867
KMeans_short	0.0	0.0	3.898321
daal4py_KMeans_short	0.0	0.0	2.114823
LogisticRegression	0.0	0.0	25.416315
daal4py_LogisticRegression	0.0	0.0	5.911150
Ridge	0.0	0.0	11.484007
daal4py_Ridge	0.0	0.0	2.494304
HistGradientBoostingClassifier	0.0	5.0	14.648287
lightgbm	0.0	5.0	10.404538
xgboost	0.0	5.0	33.241095
catboost	0.0	5.0	23.083128
total	1.0	5.0	28.323837

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.171	0.000	4.690	0.000	1	100	NaN	NaN	0.553	0.000	0.308	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.003	0.955	0.000	0.025	1	100	0.940	0.731	4.492	0.142	5.566	0.276	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.232	0.009	0.000	0.232	1	100	1.000	0.000	0.106	0.004	2.191	0.110	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.160	0.000	4.995	0.000	1	5	NaN	NaN	0.534	0.000	0.300	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.830	0.726	0.000	0.024	1	5	0.807	0.941	4.592	0.110	5.190	0.201	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.226	0.011	0.000	0.226	1	5	1.000	1.000	0.111	0.006	2.029	0.150	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.131	0.000	6.123	0.000	-1	1	NaN	NaN	0.572	0.000	0.229	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	30.582	0.000	0.000	0.031	-1	1	0.728	0.941	4.795	0.277	6.378	0.369	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.198	0.024	0.000	0.198	-1	1	0.000	1.000	0.118	0.020	1.673	0.347	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.141	0.000	5.664	0.000	-1	5	NaN	NaN	0.580	0.000	0.244	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	39.612	0.000	0.000	0.040	-1	5	0.807	0.816	4.565	0.070	8.677	0.133	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.191	0.012	0.000	0.191	-1	5	1.000	0.000	0.108	0.005	1.781	0.137	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.131	0.000	6.089	0.000	-1	100	NaN	NaN	0.582	0.000	0.226	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	38.748	0.000	0.000	0.039	-1	100	0.940	0.816	4.587	0.041	8.447	0.075	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.203	0.021	0.000	0.203	-1	100	1.000	0.000	0.107	0.004	1.894	0.210	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.129	0.000	6.219	0.000	1	1	NaN	NaN	0.570	0.000	0.226	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	17.599	0.200	0.000	0.018	1	1	0.728	0.731	4.586	0.028	3.837	0.049	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.216	0.008	0.000	0.216	1	1	0.000	0.000	0.116	0.014	1.867	0.239	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.265	0.000	1	100	NaN	NaN	0.112	0.000	0.539	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.816	0.787	0.000	0.021	1	100	0.980	0.971	1.002	0.029	20.768	0.986	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.002	0.000	0.020	1	100	1.000	1.000	0.005	0.000	4.343	0.510	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.281	0.000	1	5	NaN	NaN	0.108	0.000	0.525	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.591	0.550	0.000	0.021	1	5	0.975	0.978	1.166	0.156	17.653	2.404	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.002	0.000	0.021	1	5	1.000	1.000	0.006	0.001	3.817	0.561	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.279	0.000	-1	1	NaN	NaN	0.131	0.000	0.438	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	25.066	0.452	0.000	0.025	-1	1	0.968	0.978	1.072	0.028	23.372	0.733	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.018	0.002	0.000	0.018	-1	1	1.000	1.000	0.005	0.001	3.580	0.580	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.310	0.000	-1	5	NaN	NaN	0.104	0.000	0.497	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.432	0.000	0.000	0.033	-1	5	0.975	0.977	0.973	0.018	34.368	0.636	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.003	0.000	0.028	-1	5	1.000	1.000	0.005	0.000	5.370	0.763	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.302	0.000	-1	100	NaN	NaN	0.094	0.000	0.562	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.685	0.000	0.000	0.033	-1	100	0.980	0.977	0.992	0.033	32.957	1.099	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.029	0.003	0.000	0.029	-1	100	1.000	1.000	0.004	0.000	6.604	0.998	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.048	0.000	0.332	0.000	1	1	NaN	NaN	0.089	0.000	0.544	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	11.768	0.182	0.000	0.012	1	1	0.968	0.971	1.004	0.026	11.722	0.358	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.011	0.001	0.000	0.011	1	1	1.000	1.000	0.005	0.001	2.338	0.385	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.778	0.000	0.029	0.000	1	100	NaN	NaN	0.802	0.000	3.464	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.815	0.217	0.000	0.005	1	100	0.974	0.978	0.215	0.007	22.411	1.262	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.002	0.000	0.006	1	100	1.000	1.000	0.000	0.000	14.678	5.653	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.522	0.000	0.032	0.000	-1	5	NaN	NaN	0.776	0.000	3.248	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.789	0.031	0.000	0.001	-1	5	0.973	0.974	0.654	0.015	1.207	0.054	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	4.619	2.283	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.747	0.000	0.029	0.000	-1	1	NaN	NaN	0.745	0.000	3.688	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.452	0.014	0.000	0.000	-1	1	0.960	0.978	0.223	0.024	2.028	0.229	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	10.099	5.431	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.007	0.000	0.027	0.000	1	1	NaN	NaN	0.731	0.000	4.113	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.794	0.013	0.000	0.001	1	1	0.960	0.968	0.123	0.009	6.443	0.503	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.424	3.392	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.932	0.000	0.027	0.000	-1	100	NaN	NaN	0.717	0.000	4.090	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.634	0.103	0.000	0.003	-1	100	0.974	0.968	0.116	0.006	22.677	1.511	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.010	0.002	0.000	0.010	-1	100	1.000	1.000	0.000	0.000	35.934	19.609	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.667	0.000	0.030	0.000	1	5	NaN	NaN	0.684	0.000	3.901	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.419	0.054	0.000	0.001	1	5	0.973	0.974	0.631	0.017	2.250	0.104	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.001	0.000	2.097	0.911	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.822	0.000	0.019	0.000	1	100	NaN	NaN	0.497	0.000	1.655	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.065	0.003	0.000	0.000	1	100	0.986	0.976	0.001	0.000	45.380	14.699	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	4.898	2.067	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.834	0.000	0.019	0.000	-1	5	NaN	NaN	0.502	0.000	1.662	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.041	0.004	0.000	0.000	-1	5	0.983	0.980	0.008	0.001	5.188	0.707	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	13.435	8.370	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.766	0.000	0.021	0.000	-1	1	NaN	NaN	0.550	0.000	1.393	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.041	0.002	0.000	0.000	-1	1	0.975	0.976	0.001	0.000	31.494	8.521	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	16.271	9.195	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.772	0.000	0.021	0.000	1	1	NaN	NaN	0.490	0.000	1.575	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.039	0.002	0.000	0.000	1	1	0.975	0.969	0.001	0.000	43.763	11.742	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.018	3.624	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.793	0.000	0.020	0.000	-1	100	NaN	NaN	0.526	0.000	1.508	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.052	0.004	0.000	0.000	-1	100	0.986	0.969	0.001	0.000	58.628	19.327	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	15.541	9.048	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.779	0.000	0.021	0.000	1	5	NaN	NaN	0.505	0.000	1.541	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.042	0.002	0.000	0.000	1	5	0.983	0.980	0.008	0.001	5.535	0.749	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.404	3.434	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.647	0.0	0.742	0.000	random	NaN	30	NaN	0.353	0.0	1.833	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.0	0.308	0.000	random	0.001	30	0.002	0.000	0.0	6.389	2.846	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.0	0.000	0.002	random	1.000	30	1.000	0.000	0.0	7.898	4.354	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.696	0.0	0.690	0.000	k-means++	NaN	30	NaN	0.296	0.0	2.351	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.0	0.268	0.000	k-means++	0.001	30	0.001	0.000	0.0	7.753	3.739	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.0	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	8.219	5.127	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	7.950	0.0	3.019	0.000	random	NaN	30	NaN	4.106	0.0	1.936	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	13.068	0.000	random	0.002	30	0.001	0.000	0.0	5.308	2.394	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.0	0.015	0.002	random	1.000	30	1.000	0.000	0.0	7.986	4.213	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	7.926	0.0	3.028	0.000	k-means++	NaN	30	NaN	3.945	0.0	2.009	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	13.006	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.526	2.714	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.0	0.014	0.002	k-means++	1.000	30	1.000	0.000	0.0	8.737	4.730	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.109	0.0	0.029	0.000	random	NaN	20	NaN	0.059	0.000	1.845	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.135	0.000	random	0.000	20	-0.000	0.001	0.000	3.291	0.825	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.000	9.578	5.303	See	See
3	KMeans_short	fit	10000	10000	2	20	0.383	0.0	0.008	0.000	k-means++	NaN	20	NaN	0.143	0.000	2.680	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.147	0.000	k-means++	0.003	20	-0.001	0.001	0.000	2.713	0.718	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.000	7.482	4.036	See	See
6	KMeans_short	fit	10000	10000	100	20	0.347	0.0	0.461	0.000	random	NaN	20	NaN	0.296	0.000	1.171	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.177	0.000	random	0.232	20	0.247	0.002	0.001	1.430	0.901	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	random	1.000	20	1.000	0.000	0.000	8.002	3.488	See	See
9	KMeans_short	fit	10000	10000	100	20	1.233	0.0	0.130	0.000	k-means++	NaN	20	NaN	0.715	0.000	1.725	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.004	0.0	4.357	0.000	k-means++	0.264	20	0.359	0.002	0.000	2.277	0.404	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	k-means++	1.000	20	1.000	0.000	0.000	6.723	2.962	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	15.804	0.0	[-0.07465681]	0.000	NaN	NaN	NaN	NaN	NaN	3.018	0.0	5.236	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[41.99145527]	0.000	NaN	NaN	NaN	NaN	0.534	0.000	0.0	0.785	0.373	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.12454269]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.374	0.390	See	See
3	LogisticRegression	fit	1000	1000	10000	[28]	1.446	0.0	[-1.42091244]	0.001	NaN	NaN	NaN	NaN	NaN	1.277	0.0	1.133	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[28]	0.003	0.0	[84.08914711]	0.000	NaN	NaN	NaN	NaN	0.330	0.004	0.0	0.637	0.086	See	See
5	LogisticRegression	predict	1000	1	10000	[28]	0.000	0.0	[15.87076662]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.141	0.085	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.320	0.000	0.250	0.0	NaN	NaN	NaN	0.311	0.000	1.030	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.001	6.731	0.0	NaN	NaN	0.118	0.019	0.002	0.624	0.073	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.589	0.0	NaN	NaN	NaN	0.000	0.000	0.707	0.564	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.593	0.000	0.502	0.0	NaN	NaN	NaN	0.422	0.000	3.779	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	2.328	0.0	NaN	NaN	1.000	0.000	0.000	1.194	1.263	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.008	0.0	NaN	NaN	NaN	0.000	0.000	0.679	0.521	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}